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README.md

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+---
+license: mit
+task_categories:
+  - visual-question-answering
+  - image-to-text
+language:
+  - en
+tags:
+  - benchmark
+  - multimodal
+  - reasoning
+  - visual-grounding
+  - mllm-evaluation
+pretty_name: DailyClue
+size_categories:
+  - n<1K
+---
+
+# DailyClue Dataset
+
+**Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios**
+
+[![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2604.14041)
+[![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/your-org/DailyClue)
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+
+## Dataset Summary
+
+DailyClue is a benchmark for evaluating **visual clue-driven reasoning** in Multimodal Large Language Models (MLLMs). Unlike benchmarks that test pre-existing knowledge, DailyClue requires models to actively identify decisive visual clues from images before producing answers.
+
+The dataset spans **4 major domains** and **16 distinct subtasks**, with **666 total questions**.
+
+## Dataset Structure
+
+```
+DailyClue/
+├── daily_life/          # Images for Daily Commonsense Reasoning
+├── location/            # Images for Location Identification
+├── science/             # Images for Scientific Commonsense
+├── spatial/             # Images for Spatial Reasoning
+├── daily_life.json
+├── location.json
+├── science.json
+└── spatial.json
+```
+
+## Statistics
+
+| Category | # Questions | Formats |
+|---|---|---|
+| Daily Commonsense Reasoning | 180 | Multiple Choice, Yes/No, Open-ended |
+| Location Identification | 200 | Open-ended |
+| Spatial Reasoning | 163 | Multiple Choice, Yes/No |
+| Scientific Commonsense | 123 | Multiple Choice, Yes/No, Open-ended |
+| **Total** | **666** | |
+
+## Data Fields
+
+Each JSON entry contains:
+
+| Field | Type | Description |
+|---|---|---|
+| `image` | `list[str]` | Image filename(s) within the category subfolder |
+| `question` | `str` | The question posed to the model |
+| `clues` | `str` | Human-annotated ground-truth visual clues (see note below) |
+| `ground_truth` | `str` | The correct answer |
+| `format` | `str` | `"Multiple choice"`, `"Yes or no"`, or `"Open-ended"` |
+| `category_1` | `str` | Primary domain (one of the four above) |
+| `category_2` | `str` | Subtask within the primary domain |
+| `language` | `str` | `"English"` |
+
+> **Note on `clues`**: This field contains human-annotated ground-truth visual clues. It is used in ablation experiments (injecting GT clues to probe the impact on model accuracy) and in the Rigorous Evaluation Protocol (checking whether model-predicted clues semantically align with GT clues). It is **not** fed to the model during standard inference.
+
+## Usage
+
+### Download
+
+```bash
+# via git
+git clone https://huggingface.co/datasets/Crysun/DailyClue
+
+# via huggingface_hub
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="Crysun/DailyClue", repo_type="dataset", local_dir="./dataset")
+```
+
+### Run Inference
+
+After downloading, point the inference script to the local directory:
+
+```bash
+python infer/inference.py \
+  --dataset ./dataset \
+  --model_names "gpt-4o" \
+  --prompt_mode "b"
+```
+
+See the [GitHub repository](https://github.com/your-org/DailyClue) for the full evaluation pipeline.
+
+## Citation
+
+```bibtex
+@article{dailyclue2026,
+  title={Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios},
+  author={Li, Xiaomin and Wang, Tala and Zhong, Zichen and Zhang, Ying and Zheng, Zirui and Isobe, Takashi and Li, Dezhuang and Lu, Huchuan and He, You and Jia, Xu},
+  journal={arXiv preprint arXiv:2604.14041},
+  year={2026}
+}
+```